research-article

Generating Adaptation Rules of Software Systems: A Method Based on Genetic Algorithm

Authors:

Wenpin JiaoAuthors Info & Claims

ICMLC '18: Proceedings of the 2018 10th International Conference on Machine Learning and Computing

Pages 347 - 356

https://doi.org/10.1145/3195106.3195137

Published: 26 February 2018 Publication History

Abstract

Nowadays, applications are deployed and executed in open, uncertain and dynamic environments. Increasingly, applications are required to have the capability to automatically change its behaviors in response to changing environmental conditions. As the complexity of adaptive and autonomic systems grows, designing and managing the set of adaptation rules becomes increasingly challenging and may produce huge computation cost. If we dynamically generate adaptation rules at run time, it's difficult to deal with the changes quickly. The software system challenges the method for efficiently generating effective adaptation rules. This paper proposes an approach to combine genetic algorithm and linear regression to automatically generate adaptation rules for software systems. Unlike traditional rule-based adaptation methods, our solution enables a system to obtain a prediction function to determine the corresponding system configuration under any considered environmental condition. We have applied this genetic algorithm based approach to the dynamic reconfiguration of two different software systems. The experimental results show that our method is practical and highly-efficient in software reconfiguration under changing environmental conditions. Besides, the user's requirements can be well satisfied.

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Cited By

Gheibi OWeyns DQuin F(2021)Applying Machine Learning in Self-adaptive SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/346944015:3(1-37)Online publication date: 18-Aug-2021
https://dl.acm.org/doi/10.1145/3469440
Liu YChen ZJiao W(2018)A Multi-Goal Oriented Approach for Adaptation Rules Generation2018 25th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC.2018.00039(249-257)Online publication date: Dec-2018
https://doi.org/10.1109/APSEC.2018.00039

Index Terms

Generating Adaptation Rules of Software Systems: A Method Based on Genetic Algorithm
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Stochastic approximation

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ICMLC '18: Proceedings of the 2018 10th International Conference on Machine Learning and Computing

February 2018

411 pages

ISBN:9781450363532

DOI:10.1145/3195106

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University

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New York, NY, United States

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Published: 26 February 2018

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ICMLC 2018

ICMLC 2018: 2018 10th International Conference on Machine Learning and Computing

February 26 - 28, 2018

Macau, China

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Cited By

Gheibi OWeyns DQuin F(2021)Applying Machine Learning in Self-adaptive SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/346944015:3(1-37)Online publication date: 18-Aug-2021
https://dl.acm.org/doi/10.1145/3469440
Liu YChen ZJiao W(2018)A Multi-Goal Oriented Approach for Adaptation Rules Generation2018 25th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC.2018.00039(249-257)Online publication date: Dec-2018
https://doi.org/10.1109/APSEC.2018.00039

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